Search

This paper proposes a novel stabilizing control method aimed at overcoming the instability challenges posed by the negative incremental resistance characteristics of a constant power load (CPL) within an autonomous microgrid (MG). The proposed stabilization technique integrates a power derivative-integral term with conventional droop control, strategically applied to enhance the MG's dynamic stability in the presence of CPL. The considered MG model, encompassing three inverter-based distributed generations (DGs), a constant impedance load (CIL), and CPL, is meticulously developed and simulated using MATLAB environment. Phase Locked Loop (PLL) is employed to synchronize the CPL with MG. The proposed controller is a key highlight, featuring optimally designed and tuned controller parameters for all DGs, CPL, and PLL. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to address the challenges linked to the laborious tuning process of controllers. The simulation results, which include scenarios involving three-phase faults and step changes, provide compelling evidence of the proposed controller's superior performance compared to conventional droop scheme. Furthermore, a comparative analysis is conducted to affirm and quantify the enhancements achieved through the proposed modified droop PSO-based MG controller concerning transient response. The results obtained emphasize the effectiveness of the proposed approach in simultaneously minimizing both overshoot and settling time. In comparison with the conventional controller, the proposed controller demonstrates a substantial decrease in percentage overshoot for the active power of DG3 and DC voltage of the CPL, with values of 93.89 and 99.9%, respectively. The corresponding improvements in settling time are notable, showcasing reductions of 83.11% for the active power of DG3 and 66.1% for the DC voltage of the CPL. When compared to the GA-based controller, the proposed controller exhibits significant percentage overshoot reductions for and DC voltage of the CPL, achieving 79.42 and 99.8%, respectively. Additionally, the settling time records noteworthy improvements, with reductions of 76.19% for the active power of DG3 and 57.57% for the DC voltage of the CPL. To further validate the real-world applicability and effectiveness of the proposed method, a real-time digital simulator (RTDS) is employed. The RTDS experiments results confirm the proposed scheme's ability to enhance MG stability, substantiating the simulation findings. This holistic approach, encompassing theoretical modeling, simulation studies, and real-time validation, establishes the proposed stabilizing control method as a promising and effective solution for mitigating instability issues associated with CPL in autonomous MGs. [ABSTRACT FROM AUTHOR]

Recently, interest in wireless sensor networks (WSNs) and applications has grown significantly. The path loss caused by buildings and tall trees is a major obstacle in the distribution of sensor nodes. Because of absorption, dispersion, and attenuation by the structures or trees, a degradation occurs in the reliability of the communication link. In this paper, four path-loss models (PLMs) were developed for a LoRa (long-range) wireless sensor node mounted on the drone as a part of a WSN for monitoring air pollution over a distance of approximately 1,000 m utilising the received signal strength indicator (RSSI). Four novel PLMs were created using MATLAB's curve-fitting tools, employing polynomial (POLY), exponential (EXP), Gaussian, and power equations. To improve the accuracy of these equations, their coefficients were fine-tuned using the particle swarm optimisation (PSO) algorithm, resulting in the development of hybrid algorithms—EXP-PSO, POLY-PSO, Gaussian-PSO, and power-PSO. The results demonstrated that PSO-enhanced PLMs achieved an outstanding correlation coefficient (R2) value of 1, indicating their exceptional ability to represent PLMs accurately in the tested air population environment utilising WSNs. Notably, the POLY-PSO algorithm outperformed other algorithms, achieving the lowest root-mean-square error (RMSE) of 1.945 × 10−4 and sum of squares error (SSE) of 1.853 × 10−26. The PLMs based on hybridisation algorithms outperformed existing PLMs regarding the correlation coefficient. These findings hold significant implications for designing and optimising WSNs in air pollution applications, offering valuable insights into radio path-loss modelling in this area. [ABSTRACT FROM AUTHOR]

Energy prices are booming to high levels with the ongoing global events and massive energy demand post-COVID, triggering panic among-st the general public. In this regard, energy demand management can play an important role here by letting both the entities (utility and customer) take control over the consumption pattern long enough until renewables take over the energy supply from the conventional resources using real-time device scheduling algorithms. To achieve this phenomenon, both participating entities need proper prioritization and their interests to be properly taken care of. In this paper sensitivity analysis of varying priorities to the objectives (customer cost and peak-to-average power ratio) representing both the entities has been carried out. A real-time device scheduling algorithm is implemented to improve the consumption pattern of appliances consisting of multiple objectives. Proper way of normalization is developed to retain the original essence of these objectives. The analysis embeds novel swap operations that are incorporated with standard particle swarm optimization (PSO) and its variants under varying pricing schemes. In order to demonstrate the proposed DR-based real-time optimization, a residence in Chicago, Illinois, USA is taken as a case study for this work. The results demonstrate the quickness and proper optimizations of the PSO variants with butterfly-PSO performing the best among-st the rest. Continuous real-time pricing obtained the best solution with apt quickness among-st its counterparts for each time-slot and for cumulative time of the day. An overall reduction in each entity's metrics is obtained in this work. [ABSTRACT FROM AUTHOR]

This study introduces an asymmetric V-shaped interior permanent magnet synchronous motor (AVIPMSM) by modifying the conventional flux barrier of a V-shaped interior permanent magnet synchronous motor (VIPMSM) including optimization-based finite element analysis (FEA). Nowadays usage of VIPMSM is increasing where higher torque density with high efficiency by decreasing the torque ripple factor is required. To improve these properties, flux barriers are modified in VIPMSM by applying optimization methods. For each pole of VIPMSM, modification has been depleted based on symmetric and asymmetric barriers. It introduces the magnetic field shifting effect in the motor. After deciding the flux barrier in the rotor, an optimized method is applied for achieving the proper dimensions of each barrier to estimate the maximum torque and other performances of the motor. Here, a 24/4 pole VIPMSM is considered for design and analysis. ANSYS Maxwell platform is chosen for performance analysis of motor. In addition to using the inbuilt minimum function optimization techniques of ANSYS, genetic algorithm (GA)/particle swarm optimization (PSO) in MATLAB is applied in the proposal to optimize the flux barriers of the motor. To validate the effectiveness of the proposed method, FEA is adopted by taking into account optimized parameters. The paper also investigates the effect of flux barriers on loss factors to improve the motor efficiency. A comparative study with the benchmark VIPMSM is presented to validate the designed AVIPMSM using optimization and its results are reported. Eventually, the FEA result proves that PSO optimized model is more appropriate for the case understudies. [ABSTRACT FROM AUTHOR]

Searching for robust and discriminative features is challenging because of the high-dimensional characteristics of features. Although numerous dimension-reduction approaches have been proposed, the effect of class imbalance is yet to be investigated. However, most real-world datasets exhibit a skewed distribution of classes. Therefore, high-dimensional and class imbalanced datasets should be addressed simultaneously. This paper proposes a novel method that combined evolutionary population dynamics (EPD) with the marine predator's algorithm (MPA). Numerous novel approaches based on MPA, EPD, roulette wheel selection (RWS), and tournament selection (TS) have been proposed to address premature convergence and inadequate search capability problems of the MPA. In the first method, EPD is used to eliminate the worst solutions of the MPA and reposition them around to the top predators using different velocity ratios between the predator and prey. In the second and third approaches, RWS and TS are used to select low-fitness predators during the search process, which ensures the diversity of the selected solutions. The proposed MPA_EPD approach was applied to high-dimensional and skewed datasets. The results indicated that the EPD improved MPA by avoiding local optima and enhancing convergence rate and exploration capability. The results indicate that the area under the curve accuracy for MPA_EPD methods was higher than other filter- and wrapper-based methods, which ensures MPA capability to investigate the feature search space and select informative feature subsets. [ABSTRACT FROM AUTHOR]

This paper proposes a combination of a nonlinear feedback linearization control (FLC) and a second-order sliding mode (SOSM) controller to enhance the effectiveness and performance of variable speed control in an open-ended winding induction motor (OEWIM). The literature indicates that the traditional FLC with a PI controller is susceptible to disruptions from its internals as well as outside interference. The conventional sliding-mode controller can offer robust control. However, it suffers from the chattering phenomenon. The SOSMC, based on a super-twisting algorithm (STA), was designed and implemented to overcome all of these drawbacks. It is extremely difficult to obtain the optimum parameters for each FLC and SOSM controller that give good results using traditional methods due to the relatively large number of parameters. Moreover, a novel approach based on the linearly decreasing method for both inertia weight and learning constant of the particle swarm optimization (PSO) algorithm was presented to improve the variable speed control of the OEWIM. To maintain a constant switching frequency, decrease harmonic distortion, and reduce common-mode voltage (CMV), a space vector modulation technique of a seven-level inverter is suggested and implemented by supplying each end of an open-ended stator winding induction motor with three-level inverters. The advantages of the suggested control system have been confirmed using simulated results of various tests of the complete system. [ABSTRACT FROM AUTHOR]

The energy scheduling problem (ESP) is a complex NP-hard optimization scheduling problem of finding the best schedule for the energy consumed through a time horizon by smart appliances according to several constraints and dynamic pricing scheme(s). The primary goal of addressing ESP is to optimize electricity bills for users, the highest energy consumption through a time horizon, and the comfort level of users. Several communication and controlling techniques were utilized to interconnect and centralize the smart appliances to be scheduled, where the Internet of Things (IoT) technique is the most helpful. Several studies have proposed different optimization methods to address the ESP optimally. Unfortunately, due to the low performance of the utilized methods and the high number of the problem’s constraints, the best schedules were not achieved in many cases. In this paper, a new improved optimization method on the basis of a hybridization approach between two well-established optimization methods, called the marine predators algorithm and particle swarm optimization, is proposed to handle ESP optimally. The proposed method is mainly established and utilized to enhance the schedules with the worse fitness values to improve them to be more acceptable. Furthermore, to avoid the high complexity of the ESP constraints, Renewable Energy Sources (RESs) based on real-world dataset is utilized along the proposed method. Also, the IoT technique is utilized to establish the connection and build the scheduling system. Finally, to optimize the ESP objectives simultaneously, ESP is formulated as a multi-objective problem. In the evaluation stage, the proposed hybrid method with RESs is tested among four different comparison studies and phases, including the adapted methods with and without RESs, hybrid methods with and without RESs, and based state-of-the-arts. The proposed hybrid method with RESs proves its efficiency and high performance in all comparison phases, where it achieved the best results among all compared methods. [ABSTRACT FROM AUTHOR]